We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more!
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A Crash Course in Causality: Inferring Causal Effects from Observational Data
펜실베이니아 대학교이 강좌에 대하여
귀하가 습득할 기술
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
제공자:

펜실베이니아 대학교
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
강의 계획표 - 이 강좌에서 배울 내용
Welcome and Introduction to Causal Effects
This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.
Confounding and Directed Acyclic Graphs (DAGs)
This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.
Matching and Propensity Scores
An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.
Inverse Probability of Treatment Weighting (IPTW)
Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.
검토
- 5 stars77.25%
- 4 stars18.46%
- 3 stars2.25%
- 2 stars0.90%
- 1 star1.12%
A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA의 최상위 리뷰
A great start for those starting to explore causal inference. The somewhat dry delivery of the lectures is fully compensated by how clear and informative they are.
Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.
It will be better to give reviews of related applications in specific AI areas (e.g, computer vision, NLP, etc.) at the end of each of the sections of the lesson.
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
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